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Spiking neural network (SNN), as the next generation of artificial neural network (ANN), offer a closer mimicry of natural neural networks and hold promise for significant improvements in computational efficiency.
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2018
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2018
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S. B. Shrestha and G. Orchard, “Slayer: Spike layer error reassignment in time,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Cited alongside, same era.
A. Sengupta, Y. Ye, R. Wang, C. Liu, and K. Roy, “Going deeper in spiking neural networks: VGG and residual architectures,” Frontiers in Neuroscience , vol. 13, p. 95, 2019
2019
Cited alongside, same era.
Y. Wu, L. Deng, G. Li, J. Zhu, Y. Xie, and L. Shi, “Direct training for spiking neural networks: Faster, larger, better,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, pp. 1311–1318, 2019
2019
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E. O. Neftci, H. Mostafa, and F. Zenke, “Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks,” IEEE Signal Processing Magazine , vol. 36, no. 6, pp. 51–63, 2019
2019
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Y. Li, Y. Guo, S. Zhang, S. Deng, Y. Hai, and S. Gu, “Differentiable spike: Rethinking gradient-descent for training spiking neural networks,” Advances in Neural Information Processing Systems , vol. 34, pp. 23 426–23 439, 2021
2021
Later among the works it cites.
Y. Kim and P. Panda, “Revisiting batch normalization for training low-latency deep spiking neural networks from scratch,” Frontiers in Neuroscience , vol. 15, p. 773954, 2021
2021
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T. Bu, W. Fang, J. Ding, P. DAI, Z. Yu, and T. Huang, “Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks,” in International Conference on Learning Representations , 2022
2022
Later among the works it cites.
D. Wu, X. Yi, and X. Huang, “A little energy goes a long way: Build an energy-efficient, accurate spiking neural network from convolutional neural network,” Frontiers in Neuroscience , vol. 16, 2022
2022
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2019
Cited alongside, same era.
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation strategies from data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 113–123, 2019
2019
Cited alongside, same era.
G. Gallego, T. Delbrück, G. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. J. Davison, J. Conradt, K. Daniilidis, et al. , “Event-based vision: A survey,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 1, pp. 154–180, 2020
2020
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N. Messikommer, D. Gehrig, A. Loquercio, and D. Scaramuzza, “Event-based asynchronous sparse convolutional networks,” in European Conference on Computer Vision . Springer, pp. 415–431, 2020
2020
Cited alongside, same era.
B. Han, G. Srinivasan, and K. Roy, “RMP-SNN: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 13 558–13 567, 2020
2020
Cited alongside, same era.
N. Rathi, G. Srinivasan, P. Panda, and K. Roy, “Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
J. Kim, J. Bae, G. Park, D. Zhang, and Y. M. Kim, “N-imagenet: Towards robust, fine-grained object recognition with event cameras,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. 2146–2156, 2021
2021
Cited alongside, same era.
S. Deng and S. Gu, “Optimal conversion of conventional artificial neural networks to spiking neural networks,” in International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
C. Duan, J. Ding, S. Chen, Z. Yu, and T. Huang, “Temporal effective batch normalization in spiking neural networks,” in Advances in Neural Information Processing Systems , 2022
2022
Later among the works it cites.
J. Zhang, B. Dong, H. Zhang, J. Ding, F. Heide, B. Yin, and X. Yang, “Spiking transformers for event-based single object tracking,” in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition , pp. 8801–8810, 2022
2022
Later among the works it cites.
S. Deng, Y. Li, S. Zhang, and S. Gu, “Temporal efficient training of spiking neural network via gradient re-weighting,” in International Conference on Learning Representations , 2022
2022
Later among the works it cites.
G. Jin, X. Yi, P. Yang, L. Zhang, S. Schewe, and X. Huang, “Weight expansion: A new perspective on dropout and generalization,” Transactions on Machine Learning Research , 2022
2022
Later among the works it cites.
Y. Guo, Y. Zhang, Y. Chen, W. Peng, X. Liu, L. Zhang, X. Huang, and Z. Ma, “Membrane potential batch normalization for spiking neural networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. 19 420–19 430, 2023
2023
Closest in time.
Y. Guo, W. Peng, Y. Chen, L. Zhang, X. Liu, X. Huang, and Z. Ma, “Joint a-snn: Joint training of artificial and spiking neural networks via self-distillation and weight factorization,” Pattern Recognition , vol. 142, p. 109639, 2023
2023
Closest in time.
Z. Zhou, Y. Zhu, C. He, Y. Wang, S. YAN, Y. Tian, and L. Yuan, “Spikformer: When spiking neural network meets transformer,” in The Eleventh International Conference on Learning Representations , 2023
2023
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Z. Wang, Y. Fang, J. Cao, Q. Zhang, Z. Wang, and R. Xu, “Masked spiking transformer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. 1761–1771, 2023
2023
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2023
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2023
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2023
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Y. Zhang, X. Liu, Y. Chen, W. Peng, Y. Guo, X. Huang, and Z. Ma, “Enhancing representation of spiking neural networks via similarity-sensitive contrastive learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 15, pp. 16 926–16 934, 2024
2024
Closest in time.
Y. Guo, Y. Chen, X. Liu, W. Peng, Y. Zhang, X. Huang, and Z. Ma, “Ternary spike: Learning ternary spikes for spiking neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 11, pp. 12 244–12 252, 2024
2024
Closest in time.
Y. Ding, L. Zuo, M. Jing, P. He, and Y. Xiao, “Shrinking your timestep: Towards low-latency neuromorphic object recognition with spiking neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 10, pp. 11 811–11 819, 2024
2024
Closest in time.